• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.521-536
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• 2021

As life expectancies increase continuously over the world, the accuracy of forecasting mortality is more and more important to maintain social systems in the aging era. Currently, the most popular model used is the Lee-Carter model but various studies have been conducted to improve this model with one of them being 6-parametric factor model (6-PFM) which is introduced in this paper. To this new model, long short-term memory (LSTM) and regularized LSTM are applied in addition to vector autoregression (VAR), which is a traditional time-series method. Forecasting accuracies of several models, including the LC model, 4-PFM, 5-PFM, and 3 6-PFM's, are compared by using the U.S. and Korea life-tables. The results show that 6-PFM forecasts better than the other models (LC model, 4-PFM, and 5-PFM). Among the three 6-PFMs studied, regularized LSTM performs better than the other two methods for most of the tests.

• International Area Studies Review
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• v.20 no.1
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• pp.23-51
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• 2016

This study analyzed the expected economic effects of the Korea-GCC FTA and sought strategies for industrial cooperation. To see the economic effects of Korea-GCC FTA, we analysed the effect of the oil tariff reduction of economy by Vector Autoregression(VAR) model. The estimation results shows that following the abolishment of the tariff on crude oil imports, GDP, GNI and consumption are expected to grow by 0.212%, 0.389% and 0.238%, respectively. Meanwhile, investment, export and import are estimated to drop by 0.462%, 0.413% and 0.342%, respectively. As for prices, producer prices are to rise by 6.356%p, whereas consumer prices fall by 2.996%p. In short, the Korea-GCC FTA and resultant abolishment of the tariff on crude oil imports followed by the decline in crude oil prices will result in declining prices whilst macroeconomic indices, such as GDP, GNI and consumption, will increase exerting positive effects on domestic economic growth. Also, it is necessary to proactively respond to GCC member states' industrial diversification policies for FTA-based industrial cooperation to diversify the sources of crude oil and natural gas imports for further resource risk management.

• Information Systems Review
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• v.19 no.1
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• pp.123-145
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• 2017

This study empirically examines the effects of regulation on online gaming. Going beyond ad hoc heuristic approaches on individual behavior, we investigate the effects of regulation on dynamic changes of games or service providers. In particular, we propose three theoretical perspectives: social influence to investigate the regulation effect, the role of prior experience to determine the difference in the regulation effect size through users' prior experience, and network externalities to discover the difference in the regulation effect size according to the number of users on an online gaming platform. We use the vector autoregression methodology to model patterns of the co-movement of online games and to forecast game usage. We find that online gamers are heterogeneous. Therefore, policy makers should make suitable regulations for each heterogeneous group to effectively avoid generating gaming addicts without interrupting the economic growth of the online gaming industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.628-636
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• 2021

This paper contains the implementation and performance evaluation results of a system that collects environmental data at the time when the wild animal intrusion occurred at farms and then predicts future wild animal intrusions through a machine learning-based Vector Autoregression(VAR) model. To collect the data for intrusion prediction, an IoT-based hardware prototype was developed, which was installed on a small farm located near the school and simulated over a long period to generate intrusion events. The intrusion prediction service based on the implemented VAR model provides the date and time when intrusion is likely to occur over the next 30 days. In addition, the proposed system includes the function of providing real-time notifications to the farmers mobile device when wild animals intrusion occurs in the farm, and performance evaluation was conducted to confirm that the average response time was 7.89 seconds.

• Korean Journal of Construction Engineering and Management
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• v.25 no.4
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• pp.53-65
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• 2024

During COVID-19 the housing market in Korea experienced the soaring prices, despite the decrease in the economic growth rate. This paper aims to analyze macroeconomic determinants affecting housing prices in Korea during the pandemic and find an appropriate statistic model to forecast the changes in housing prices in Korea. First, an appropriate lag for the model using Akaike information criterion was found. After the macroeconomic factors were checked if they possess the unit root, the dependencies in the model were analyzed using vector autoregression (VAR) model. As for the prediction, the VAR model was used and, besides, compared afterwards with the long short-term memory (LSTM) model. CPI, mortgage rate, IIP at lag 1 and federal funds effective rate at lag 1 and 2 were found to be significant for housing prices. In addition, the prediction performance of the LSTM model appeared to be more accurate in comparison with the VAR model. The results of the analysis play an essential role in policymaker perception when making decisions related to managing potential housing risks arose during crises. It is essential to take into considerations macroeconomic factors besides the taxes and housing policy amendments and use an appropriate model for prices forecast.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.91-101
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• 2021

The paper examines the impact of the COVID-19 pandemic on the stock market prices. The vector autoregression model (VAR) has been used in this analysis to survey 341 stocks on the Ho Chi Minh City Stock Exchange (HOSE) for the period from January 23, 2020 to December 31, 2020. The empirical results obtained from the analysis of 11 economic sectors suggest that there is a statistically significant impact relationship between COVID-19 and the healthcare and utility industries. Additional findings show a statistically significant negative impact of COVID-19 on the utility share price at lag 1. Analysis of impulse response function (IRF) and forecast error variance decomposition (FEVD) show an inverse reaction of utility stock prices to the impact of COVID-19 and a gradual disappearing shock after two steps. Major findings show that there is a clear negative effect of the COVID-19 pandemic on share prices, and the daily increase in the number of confirmed cases, indicate that, in future disease outbreaks, early containment measures and positive responses are necessary conditions for governments and nations to protect stock markets from excessive depreciation. Utility stocks are among the most severely impacted shares on financial exchanges during a pandemic due to the high risk of immediate or irreversible closure of manufacturing lines and poor demand for basic amenities.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1137-1147
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• 2022

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.

• Journal of Korea Trade
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• v.25 no.1
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• pp.1-17
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• 2021

Purpose - The global trend of protectionism has expanded since the onset of US President Donald Trump's administration in 2017. This global phenomenon has led to a significant reduction in world trade volume and a negative impact on economic development in some countries where the external sector accounts for a large proportion of GDP. Although Korea is a country vulnerable to this deteriorating trade environment, few studies have examined the relationship between protectionism and its business cycles based on Korean data. Thus, this paper investigates the impact of protectionism on Korea's business cycle. Design/methodology - To identify future implications, we conduct a structural vector autoregression (VAR) analysis using monthly Korean data from 1994 to 2015. Macroeconomic variables in the model include the industrial production index, inflation rates, exports (or net exports), interest rates, and exchange rates. For the identification of the shock reflecting the expansion of protectionism, we use an antidumping investigation (ADI) data. Since ADIs are followed generally by the imposition of antidumping tariffs, they have no contemporaneous impact on tariffs and are also contemporaneously exogenous to other endogenous variables in the VAR model. We examine two kinds of ADI shocks i) shocks on Korean exports imposed by Korea's trading partners (ADI-imposed shocks) and ii) shocks on imports imposed by the Korean government (ADI-imposing shocks). Findings - We find that Korea's exports decline sharply due to ADI-imposed shocks; the lowest point at the third month after the initial shock; and do not recover until 24 months later. Simultaneously, the inflation rate decreases. Therefore, the ADI-imposed shock can be regarded as a negative shock on the demand curve where both production and price decrease. In contrast, the ADI-imposing shock generates a different response. The net exports decline, but the inflation rate increases. These can be seen as standard responses with respect to the negative shock on the supply curve. Originality/value - We shed light on the relationship between protectionism and Korea's economic fluctuations, which is rarely addressed in previous studies. We also consider the effects of both protective policy measures on imports to Korea imposed by the Korean government and on policy measures imposed by Korea's trading partner countries on its exports.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.631-644
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• 2022

This paper considers robust estimation of the sparse vector autoregressive model (sVAR) useful in high-dimensional time series analysis. First, we generalize the result of Xu et al. (2008) that the adaptive lasso indeed has robustness in sVAR as well. However, adaptive lasso method in sVAR performs poorly as the number and sizes of outliers increases. Therefore, we propose new robust estimation methods for sVAR based on least absolute deviation (LAD) and Huber estimation. Our simulation results show that our proposed methods provide more accurate estimation in turn showed better forecasting performance when outliers exist. In addition, we applied our proposed methods to power usage data and confirmed that there are unignorable outliers and robust estimation taking such outliers into account improves forecasting.

• The Journal of Asian Finance, Economics and Business
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• v.4 no.4
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• pp.61-66
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• 2017

Environmental awareness and its relation to the development of economy has garnered increased attention in recent years. Researchers, over the years, have argued that sustainable development warrants for minimizing environmental degradation since one depends on the other. This study analyzes the relationship between environmental degradation (carbon emission taken as proxy for degradation), economic growth, total energy consumption and industrial production index growth in Bangladesh from year 1998 to 2013. This study uses Vector Autoregression (VAR) Model and variance decomposition of VAR to analyze the effect of these variables on carbon emission and vice-versa. The findings of VAR model suggest that industrial production and GDP per capita has significant relationship with carbon emission. Further analysis through variance decomposition shows carbon emission has consistent impact on industrial production over time, whereas, industrial production has high impact on emission in the short run which fades in the long run which is consistent with Environmental Kuznets Curve (EKC) hypothesis. Carbon emission rising along with GDP per capita and at the same time having low impact in the long run on industrial index indicates there may be other sources of pollution introduced with the rise in income of the economy over time.